o
    
sh                     @   s   d dl mZmZmZ ddlmZ erddlmZ ddlm	Z	m
Z
mZmZ ddlmZ e r1d dlZeeZG d	d
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eZdS )    )TYPE_CHECKINGAnyOptional   )HfQuantizer   )PreTrainedModel)is_accelerate_availableis_eetq_availableis_torch_availablelogging)get_module_from_nameNc                       s   e Zd ZdZdZdZddgZ fddZdd	 Zd&ddZ	ddddde
dee
ef fddZ	d'ddddde
dddee
ef deee
  fddZd(ddZ	d'dddeee
  fd d!Zd'd"d#Zedefd$d%Z  ZS ))EetqHfQuantizera  
    8-bit quantization from EETQ quantization method:
        before loading: converts transformer layers into W8A16Linear during loading: load 16bit weight and pass to the
        layer object after: quantizes individual weights in Linear8bitLt into 8bit at first .cuda() call
    TFeetq
acceleratec                    s   t  j|fi | || _d S N)super__init__quantization_config)selfr   kwargs	__class__ d/var/www/html/alpaca_bot/venv/lib/python3.10/site-packages/transformers/quantizers/quantizer_eetq.pyr   -   s   
zEetqHfQuantizer.__init__c              
   O   s   t  stdzdd l}W n ty% } zdt|v r td| d }~ww t s-td|dds9|ddr=td	tj	 sFt
d
|d}|d u rVtd d S |d urot|trqd| v skd| v rstdd S d S d S )NzUsing `eetq` 8-bit quantization requires eetq.Please install the latest version of eetq from : https://github.com/NetEase-FuXi/EETQr   shard_checkpointzYou are using a version of EETQ that is incompatible with the current transformers version. Either downgrade transformers to <= v4.46.3 or, if available, upgrade EETQ to > v1.0.0.zNLoading an EETQ quantized model requires accelerate (`pip install accelerate`)from_tfF	from_flaxzConverting into 8-bit weights from tf/flax weights is currently not supported, please make sure the weights are in PyTorch format.z/No GPU found. A GPU is needed for quantization.
device_mapzYou have loaded an EETQ model on CPU and have a CUDA device available, make sure to set your model on a GPU device in order to run your model.cpudiskzYou are attempting to load an EETQ model with a device_map that contains a CPU or disk device. This is not supported. Please remove the CPU or disk device from the device_map.)r
   ImportErrorr   strr	   get
ValueErrortorchcudais_availableRuntimeErrorloggerwarning_once
isinstancedictvalues)r   argsr   r   excr   r   r   r   validate_environment1   sH   

"z$EetqHfQuantizer.validate_environmentdtypetorch.dtypereturnc                 C   s6   |d u rt j}td| |S |t jkrtd |S )NzOverriding dtype=%s with `dtype=torch.float16` due to requirements of `eetq` to enable model loading in 8-bit. Pass your own dtype to specify the dtype of the remaining non-linear layers or pass dtype=torch.float16 to remove this warning.zLWe suggest you to set `dtype=torch.float16` for better efficiency with EETQ.)r%   float16r)   info)r   r1   r   r   r   update_dtype_   s   	

zEetqHfQuantizer.update_dtypemodelr   param_valueztorch.Tensor
param_name
state_dictc           	      K   sj   ddl m} t||\}}t||r3| js|dkr)|dkr'|jtjkr'tddS |dkr1tdd	S dS )
Nr   )
EetqLinearbiasweightz6Expect quantized weights but got an unquantized weightFweight_scalez;Expect unquantized weights but got a quantized weight_scaleT)	r   r;   r   r+   pre_quantizedr1   r%   int8r$   )	r   r7   r8   r9   r:   r   r;   moduletensor_namer   r   r   check_quantized_paramm   s   
z%EetqHfQuantizer.check_quantized_paramNtarget_deviceztorch.deviceunexpected_keysc                 C   sL   ddl m} t||\}}	||\}
}|
||j|	< |d|| dS )zB
        quantizes weights into qweight and weight_scales
        r   )quantize_and_preprocess_weightsweight_scalesN)r   rF   r   to_buffersregister)r   r7   r8   r9   rD   r:   rE   rF   rA   rB   	new_valuer>   r   r   r   create_quantized_param   s
   z&EetqHfQuantizer.create_quantized_paramc                 K   s   |S r   r   )r   r7   r   r   r   r   #_process_model_after_weight_loading      z3EetqHfQuantizer._process_model_after_weight_loadingkeep_in_fp32_modulesc                 K   sD   ddl m} | || jj|| _||| j| j| jd}| j|j_d S )Nr   )replace_with_eetq_linear)modules_to_not_convertr   r?   )integrationsrP   get_modules_to_not_convertr   rQ   r?   config)r   r7   rO   r   rP   r   r   r   $_process_model_before_weight_loading   s   
z4EetqHfQuantizer._process_model_before_weight_loadingc                 C      dS NTr   )r   safe_serializationr   r   r   is_serializable   rN   zEetqHfQuantizer.is_serializablec                 C   rV   rW   r   )r   r   r   r   is_trainable   s   zEetqHfQuantizer.is_trainable)r1   r2   r3   r2   r   )r7   r   )__name__
__module____qualname____doc__ requires_parameters_quantizationrequires_calibrationrequired_packagesr   r0   r6   r"   r,   r   rC   r   listrL   rM   rU   rY   propertyboolrZ   __classcell__r   r   r   r   r   !   sR    
.








r   )typingr   r   r   baser   modeling_utilsr   utilsr	   r
   r   r   quantizers_utilsr   r%   
get_loggerr[   r)   r   r   r   r   r   <module>   s   
